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--- |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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--- |
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## Setup |
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Install the latest version of `diffusers` |
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```shell |
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pip install git+https://github.com/huggingface/diffusers.git |
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``` |
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Login to your Hugging Face account |
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```shell |
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hf auth login |
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``` |
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## How to use |
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The following code snippet demonstrates how to use the [Flux2](https://huggingface.co/black-forest-labs/FLUX.2-dev) modular pipeline with a remote text encoder and a 4bit quantized version of the DiT. It requires approximately 19GB of VRAM to generate an image. |
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```python |
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import torch |
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from diffusers.modular_pipelines.flux2 import ALL_BLOCKS |
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from diffusers.modular_pipelines import SequentialPipelineBlocks |
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blocks = SequentialPipelineBlocks.from_blocks_dict(ALL_BLOCKS['remote']) |
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pipe = blocks.init_pipeline("diffusers/flux2-bnb-4bit-modular") |
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pipe.load_components(torch_dtype=torch.bfloat16, device_map="cuda") |
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prompt = "a photo of a cat" |
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outputs = pipe(prompt=prompt, num_inference_steps=28, output="images") |
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outputs[0].save("flux2-bnb-modular.png") |
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``` |
